Optimization of a finite element code implemented in MATLAB: On the use of GPUs for High Performance Computing

D. Jose Manuel Navarro Jimenez
Department of Mechanical and Materials Engineering, Universitat Politecnica de Valencia
Universitat Politecnica de Valencia, 2014

   title={Optimization of a finite element code implemented in MATLAB. On the use of GPUs for High Performance Computing},

   author={Navarro Jim{‘e}nez, Jos{‘e} Manuel},



Download Download (PDF)   View View   Source Source   



The Department of Mechanical and Materials Engineering has developed a 2D Finite Element code based on geometry independent Cartesian grids (cgFEM) capable of solving shape optimization problems as well as making patientspecific analyses using medical images. A similar code in 3D (FEAVox) is currently under development. Both codes are implemented in MATLAB, a simple and intuitive programming language but with a higher computational cost than compiled languages such as C++ or FORTRAN. The objective of this Thesis is to develop programming procedures to improve the performance of the existing and the currently under development software. Among other optimization techniques this Thesis will focus on the use of Graphics Processing Units (GPU) for high performance computing. The use of these techniques has led to a software that, despite being implemented with MATLAB, improves the computational efficiency of commercial software which is developed using compiled programming languages.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

TwitterAPIExchange Object
    [oauth_access_token:TwitterAPIExchange:private] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
    [oauth_access_token_secret:TwitterAPIExchange:private] => o29ji3VLVmB6jASMqY8G7QZDCrdFmoTvCDNNUlb7s
    [consumer_key:TwitterAPIExchange:private] => TdQb63pho0ak9VevwMWpEgXAE
    [consumer_secret:TwitterAPIExchange:private] => Uq4rWz7nUnH1y6ab6uQ9xMk0KLcDrmckneEMdlq6G5E0jlQCFx
    [postfields:TwitterAPIExchange:private] => 
    [getfield:TwitterAPIExchange:private] => ?cursor=-1&screen_name=hgpu&skip_status=true&include_user_entities=false
    [oauth:protected] => Array
            [oauth_consumer_key] => TdQb63pho0ak9VevwMWpEgXAE
            [oauth_nonce] => 1477371105
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1477371105
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => hOtI57VtQK7SIREP7jyYHCR2Ibc=

    [url] => https://api.twitter.com/1.1/users/show.json
Follow us on Facebook
Follow us on Twitter

HGPU group

2033 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: